U.S. patent number 6,691,061 [Application Number 09/350,973] was granted by the patent office on 2004-02-10 for method and apparatus for monitoring operational performance of fluid storage systems.
This patent grant is currently assigned to Warren Rogers Associates, Inc.. Invention is credited to John R. Collins, Jillanne B. Jones, Warren F. Rogers.
United States Patent |
6,691,061 |
Rogers , et al. |
February 10, 2004 |
**Please see images for:
( Certificate of Correction ) ** |
Method and apparatus for monitoring operational performance of
fluid storage systems
Abstract
A method and apparatus for monitoring a fluid storage and
dispensing system such as a fuel storage tank includes a dispensing
apparatus which dispenses a quantity of fluid from the system based
on an authorization, e.g., provided by a fuel access control
system. Measurement data from the dispensing apparatus are
collected in a form readable by a computer and stored in a memory.
The stored measurement data are statistically analyzed to calculate
a volume of fluid based on the measurement data collected from the
dispensing apparatus.
Inventors: |
Rogers; Warren F. (Newport,
RI), Collins; John R. (Punta Gorda, FL), Jones; Jillanne
B. (Narragansett, RI) |
Assignee: |
Warren Rogers Associates, Inc.
(Middletown, RI)
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Family
ID: |
30772410 |
Appl.
No.: |
09/350,973 |
Filed: |
July 9, 1999 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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083732 |
May 22, 1998 |
6401045 |
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658139 |
Jun 4, 1996 |
5757664 |
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PCTUS9709505 |
Jun 4, 1997 |
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Current U.S.
Class: |
702/156 |
Current CPC
Class: |
G01F
23/0069 (20130101); G01F 23/0076 (20130101) |
Current International
Class: |
G01F
23/00 (20060101); G01B 011/28 (); G01B
013/20 () |
Field of
Search: |
;702/179,155,156
;700/232,237,239,236 ;340/605 ;235/381 ;705/30,416 ;186/53 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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0310298 |
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May 1989 |
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EP |
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2 600 318 |
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Dec 1987 |
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FR |
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2124390 |
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Feb 1984 |
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GB |
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2138947 |
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Oct 1984 |
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GB |
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97/46855 |
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Dec 1997 |
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WO |
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Other References
Search Report dated Nov. 24, 2000. .
EPA Pub. No. 510-K-95-003, "Straight Talk on Tanks: Leak Detection
Methods for Petroleum Underground Storage Tanks and Piping" (Jul.
1991). .
William E Boyce and Richard C. DiPrima, "Elementary Differential
Equations and Boundary Value Problems" Rensrelaer Polytechnic
Institute (Third Edition). .
W.F. Rogers, "Volumetric Leak Detection-A Systems Perspective",
ASTM STP 1161 (1992). .
Ken Wilcox Assoc., Inc., "Evaluation of the W.R. Associates SIRA
Statistical Inventory Reconciliation System (Version 5.2)" Final
Report, Feb. 17, 1995, 19 pages. .
EPA Pub. No. 510-B-95-009, "Introduction to Statistical Inventory
Reconciliation," (Sep. 1995)..
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Primary Examiner: Barlow; John
Assistant Examiner: Pretlow; Demetrius R.
Attorney, Agent or Firm: Kirkpatrick & Lockhart LLP
Parent Case Text
CROSS REFERENCE TO RELATED APPLICATIONS
This is a continuation-in-part of Ser. No. 09/083,732, filed May
22, 1998, now U.S. Pat. No. 6,401,045 which is a
continuation-in-part of application Ser. No. 08/658,139, filed Jun.
4, 1996, now U.S. Pat. No. 5,757,664, and a continuation-in-part of
International patent application no. PCT/US97/9505, with an
international filing date of Jun. 4, 1997.
Claims
What is claimed is:
1. A method of monitoring a fluid storage and dispensing system
including a dispensing apparatus, the method comprising: dispensing
a quantity of fluid from the system using the dispensing apparatus
based on an authorization; collecting a plurality of measurement
data from the dispensing apparatus in a format readable by a
computer, wherein the plurality of measurement data is stored in
matrix format; collecting a plurality of dispensing apparatus
activity data including a state of the dispensing apparatus in a
format readable by a computer; storing the plurality of measurement
data and the plurality of dispensing apparatus activity data in a
memory; statistically analyzing the stored plurality of measurement
data to calculate a volume of fluid based on the plurality of
measurement data collected from the dispensing apparatus; and
auditing the quantity of fluid dispensed from the system based on
the plurality of dispensing apparatus activity data.
2. The method of claim 1 wherein the authorization is based on
information relating to a user requesting that the quantity of
fluid be dispensed from the system.
3. The method of claim 1 wherein the authorization is provided by a
fuel access control system.
4. The method of claim 1 further comprising transmitting the
plurality of measurement data to a processor to perform the
statistically analyzing step.
5. The method of claim 1 wherein the dispensing apparatus includes
a totalizer and a meter.
6. The method of claim 1 wherein the plurality of measurement data
is stored in compressed matrix format.
7. The method of claim 6 wherein the compressed matrix is a product
of a data matrix and a transpose of the data matrix.
8. A method of monitoring a fluid storage and dispensing system
including a plurality of dispensing apparatus, the method
comprising: dispensing a quantity of fluid from the system using
the plurality of dispensing apparatus based on an authorization;
simultaneously collecting measurement data and dispensing apparatus
activity data from the plurality of dispensing apparatus in a form
readable by a computer, wherein the dispensing apparatus activity
data includes a state of the dispensing apparatus; repeating the
collecting step to obtain a plurality of the measurement data;
storing the plurality of measurement data in a memory, wherein the
plurality of measurement data is stored in matrix format;
statistically analyzing the stored plurality of measurement data to
calculate a volume of fluid based on the plurality of measurement
data collected from the dispensing apparatus; and auditing the
quantity of fluid dispensed from the system based on the dispensing
apparatus activity data.
9. The method of claim 8 wherein the authorization is based on
information relating to a user requesting that the quantity of
fluid be dispensed from the system.
10. The method of claim 8 wherein the authorization is provided by
a fuel access control system.
11. The method of claim 8 further comprising transmitting the
plurality of measurement data to a processor to perform the
statistically analyzing step.
12. The method of claim 8 wherein the plurality of measurement data
is stored in compressed matrix format.
13. The method of claim 12 wherein the compressed matrix is a
product of a data matrix and a transpose of the data matrix.
14. A fluid storage and dispensing system, comprising: a tank for
storing fluid; a dispenser for dispensing fluid from the tank; a
fuel access control system for enabling the dispenser based on an
authorization; and measurement apparatus for collecting measurement
data corresponding to a quantity of fluid dispensed from the tank
by the dispenser and for collecting dispensing apparatus activity
data including a state of the dispensing apparatus; and a processor
for statistically analyzing the measurement data to calculate a
volume of fluid in the system, wherein the processor stores the
measurement data in a matrix format.
15. The system of claim 14 further comprising a memory for storing
the collected measurement data.
16. The system of claim 14 wherein the fuel access system is
activated by a coded medium which contains identification
information pertaining to the user and which constitutes a request
to dispense fluid from the system.
17. The system of claim 14 wherein the authorization is based on
information about a user requesting to dispense fluid from the
system.
18. The system of claim 14 wherein the measurement apparatus
comprises a totalizer and a meter.
19. The system of claim 14 wherein the processor stores the
measurement data in a compressed matrix format.
20. The system of claim 19 wherein the compressed matrix is a
product of a data matrix and a transpose of the data matrix.
Description
BACKGROUND OF THE INVENTION
The invention relates to monitoring the operational performance of
fluid storage systems.
Large quantities of liquids and similar materials are often stored
in bulk storage containers or tanks, which may be located
above-ground, partially above-ground, or completely below ground.
Such containers or tanks are generally connected by piping to
flow-meters or dispensers.
For example, underground storage tanks (UST's) and, occasionally,
above-ground storage tanks (AST's) are used to store petroleum
products and fuel to be dispensed at automobile service stations,
trucking terminals, automobile rental outlets, and similar
operations through gasoline, diesel, or kerosene dispensing pumps.
Fuel product is generally delivered to such facilities by a gravity
drop from a compartment in a wheeled transport means such as a fuel
delivery truck or an introduction of product through an underground
piping system. AST's or UST's are often located at central
distribution locations so that product can be subsequently
withdrawn from the tank system to be transported for delivery to a
variety of such facilities. A distribution location with UST's or
AST's may receive deliveries of product from, e.g., a pipeline
spur, wheeled transport, a barge, or a rail car.
Direct observation of the operating condition of such tanks and
storage containers is difficult or impossible. The various methods
for identifying the amount of product in tank systems have varying
levels of accuracy, repeatability, and performance. Moreover, the
accuracy of devices which measure the amount of product dispensed
from the storage containers and tanks differs greatly, and may or
may not be temperature compensated. The amount of product actually
delivered to the tank system is often measured inaccurately and,
frequently, not at all. Rather, the owner or operator of the tank
or vessel usually records the invoiced amount of product delivered
as the actual amount introduced to the tank system, without having
any means of confirming whether the invoiced amount of product
delivered is correct.
Consequently, effective management of such facilities is
complicated by the numerous errors in the various measuring devices
and procedures used to establish a baseline for management,
planning and decisionmaking. Effective management requires the
following: 1. Accurate measurement of the volume stored in the
system. 2. Accurate determination of the volume dispensed from the
system. 3. Accurate determination of the amount of product
introduced into the system. 4. Identification of volumes added to
or removed from the tank system which are not otherwise recorded.
5. Rapid identification of leakage from the tank system. 6.
Continuous monitoring and diagnosis of the operating performance of
all of the component measuring devices of the system. 7. Continuous
analysis of sales data to predict demands of product from the
system. 8. Determination of optimal reorder times and quantities as
a function of ordering, transportation, holding, and penalty costs
in order to minimize total costs of operation and/or to maximize
profits.
Traditionally, these functions were performed crudely or, in many
cases, not at all. Volume measurements were, and in many instances
still are, based on imperfect knowledge of the geometry,
dimensions, and configuration of the storage vessel. Also,
dispensing meters are frequently miscalibrated. This is true even
when tank systems are regulated, due to the breadth of tolerance
permitted for individual sales as related to total tank volume. For
example, deliveries from the delivery vehicle are almost always
unmetered, additions of product from defueling vehicles are
typically undocumented, and theft of the product is not
uncommon.
Leakage of product has, in recent years, assumed a dimension far in
excess of the mere loss of the product. Environmental damage can,
and frequently does, expose the operator to very large liabilities
from third party litigation in addition to U.S. Environmental
Protection Agency (EPA)-mandated remediation which can cost in the
range of hundreds of thousands of dollars. The EPA's requirements
for leak detection are set forth in EPA Pub. No. 510-K-95-003,
Straight Talk On Tanks: Leak Detection Methods For Petroleum
Underground Storage Tanks and Piping (July 1991), which is
incorporated herein by reference.
To address these concerns, Statistical Inventory Reconciliation
(SIR) was developed. The SIR method consists of a computer-based
procedure which identifies all of the sources of error noted above
by statistical analysis of the various and unique patterns that are
introduced into the inventory data and, in particular, into the
cumulative variances in the data when viewed as functions of
product height, sales volumes, and time.
SUMMARY OF THE INVENTION
The present invention relates to an automatic SIR system that may
continuously and automatically collect data from completely
above-ground, partially above-ground, and completely below ground
containers for statistical analysis. The invention addresses a
variety of physical, business, operational and environmental issues
associated with the bulk storage of liquids or pourable solids.
The present invention is an application of SIR that greatly
enhances the ability to manage a facility effectively. It provides
the means to characterize exactly the geometry, dimensions, and
configuration of the storage vessel, identify overages and
shortages in deliveries and unexplained additions and removals of
product, and provide an accurate assessment of overall dispensing
meter calibration. In addition, by accounting for such
discrepancies, the present invention permits identification of
leakage at rates less than 0.1 gallon per hour in all of its
estimates to any prescribed tolerance. By increasing the number of
measurements taken, the estimates can be derived at any desired
level of tolerance.
The method of the present invention makes no assumptions as to the
precision of any of the measuring devices used in various system
configurations. Precision and calibration accuracies are derived
from the data alone. Also, it is not assumed that the tank system
is leak free; the leak status of the system is determined from the
data alone.
The method derives tank geometry, dimensions, and configuration,
and their impact on the totality of cumulative inventory variances,
as a function of product height in the tank. Correctness of
dispensing meter calibration is verified in a similar manner by
testing for randomness of cumulative variances as a function of
varying sales volumes. Having confirmed that such remaining
residual variances are random, reflecting only the inherent random
noise of the measurement devices, the present method analyzes
departures of the cumulative variance from the bounds determined by
the calculated random noise level. All calculations as to the
volumes added, removed, metered or leaking are based upon extended
successive, simultaneous observations of meter and gauge readings.
The number of observations incorporated in each such calculation is
determined by computing confidence bands for the parameters of
interest and extending data collection as necessary to achieve
predetermined tolerances.
For example, the method of the present invention is capable of
distinguishing between continuous losses consistent with leakage
and one-time unexplained removals of the fluid product from the
tank. The method may be used to ensure the accuracy of computed
delivery volumes, which are determined and reported with confidence
boundaries calculated for estimated delivered quantities.
The method can also be used to control and monitor the accuracy of
purchase costs of fluids such as petroleum which are delivered to
tanks. For example, motor fuel retailers may be charged by
wholesalers for either net or gross volumes purported to have been
delivered. A determination that purchase charges are appropriate
thus requires frequent simultaneous readings of sales, tank volumes
and temperatures, which can be accomplished using the method of the
present invention.
To accomplish these goals, the present invention involves
estimating changes in product volume in a tank based on multiple
data points and their respective likely errors measured
continuously over a period of time. A software program is used to
implement an algorithm that employs concepts from matrix theory and
mathematical statistics. The algorithm includes generating the
product of a matrix and its transpose by successive additions of
partial products of partitions of the matrix and their
corresponding transposed matrix partitions to minimize the storage
requirements of the data collected. The compressed matrix data
constitutes a complete and sufficient statistic for the parameters
of interest. The algorithm thus permits the accumulation and
storage of a large amount of data in a condensed form without
sacrificing statistically useful information, to obtain a
statistically significant result with the required accuracy and
reliability.
In general, in one aspect, the invention features a method of
monitoring a fluid storage and dispensing system including a
dispensing apparatus. A quantity of fluid is dispensed from the
system using the dispensing apparatus based on an authorization. A
plurality of measurement data is collected from the dispensing
apparatus in a form readable by a computer. The plurality of
measurement data is stored in a memory. The stored plurality of
measurement data are statistically analyzed to calculate a volume
of fluid based on the plurality of measurement data collected from
the dispensing apparatus.
Implementations of the invention may also include One or more of
the following features. The authorization May be based on
information relating to a user requesting that the quantity of
fluid be dispensed from the system. The authorization may be
provided by a fuel access control system.
The method may further include transmitting the plurality of
measurement data to a processor to perform the statistically
analyzing step. The plurality of measurement data may be stored in
a compressed matrix format, which may be a product of a data matrix
and a transpose of the data matrix. The dispensing apparatus may
include a totalizer and a meter.
In general, in another aspect, the invention features a method of
monitoring a fluid storage and dispensing system including a
plurality of dispensing apparatus. A quantity of fluid is dispensed
from the system using the plurality of dispensing apparatus based
on an authorization. Measurement data is simultaneously collected
from the plurality of dispensing apparatus in a form readable by a
computer. The collecting step is repeated to obtain a plurality of
the measurement data, which are stored in a memory. The stored
plurality of measurement data is statistically analyzed to
calculate a volume of fluid based on the plurality of measurement
data collected from the plurality of dispensing apparatus.
Implementations of the invention may also include one or more of
the following features. The authorization may be based on
information relating to a user requesting that the quantity of
fluid be dispensed from the system. The authorization may be
provided by a fuel access control system.
The method may further include transmitting the plurality of
measurement data to a processor to perform the statistically
analyzing step. The plurality of measurement data may be stored in
a compressed matrix format, which may be a product of a data matrix
and a transpose of the data matrix.
In general, in another aspect, the invention features a fluid
storage and dispensing system including a tank for storing fluid
and a dispenser for dispensing fluid from the tank. A fuel access
control system enables the dispenser based on an authorization.
Measurement apparatus collects measurement data corresponding to a
quantity of fluid dispensed from the tank by the dispenser. A
processor statistically analyzes the measurement data to calculate
a volume of fluid in the system.
Implementations of the invention may also include one or more of
the following features. The system may further include a memory for
storing the collected measurement data. The measurement apparatus
may include a totalizer and a meter.
The fuel access control system may be activated by a coded medium
which contains identification information pertaining to the user
and which constitutes a request to dispense fluid from the system.
The authorization may be based on information about a user
requesting to dispense fluid from the system.
The processor may store the measurement data in a compressed data
matrix format, which may be a product of a data matrix and a
transpose of the data matrix.
An advantage of the present invention that the accuracy and
consistency of devices used to measure volume of product added to,
removed from, and present in a fluid storage system may be
determined.
Another advantage of the present invention is that additions of
material to the system, but not recorded as such, and volumes of
product removed from the system which are not registered by
measuring devices or otherwise recorded, may be identified.
Another advantage of the present invention is that discrete
one-time unrecorded removals of product from the system may be
distinguished from continuous losses consistent with leakage.
Another advantage of the present invention is that product leakage
from all parts of the system, extending from the fill point to the
point of discharge, may be identified and confirmed.
Another advantage of the present invention is that secular,
seasonal trends and repetitive special demands to provide short and
long term estimates for demand of the product, and optimal reorder
quantities and delivery schedules, may be identified and
determined.
Another advantage of the present invention is that a better
estimation of product volumes and volume changes may be obtained by
calculating the differential of the volume function.
Another advantage of the present invention is that a fuel access
control system may be used to monitor dispensing activity from and
control access to the fluid storage system.
A further advantage of the present invention is that all of the
foregoing may be accomplished in a fully automated system that
requires no human intervention, other than as an option available
to the operator to enter quantities of material reportedly
delivered for comparison with those computed.
Other features and advantages of the invention will become apparent
from the following detailed description, and from the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a schematic diagram of a facility including an
underground tank storage system.
FIGS. 2, 3 and 4 are a portion of the Mathcad computer code used to
perform the data compression algorithm.
FIGS. 5, 6 and 7 are a block diagram of the steps performed during
routine operation of the algorithm of the present invention.
FIG. 8 is a block diagram of the steps performed during the data
deletion operation of the algorithm of the present invention.
FIG. 9 is a block diagram of the steps performed during the
delivery calculation operation of the algorithm of the present
invention.
FIG. 10 is a schematic diagram of a data acquisition and
transmission network that may be used in conjunction with the
present invention.
FIG. 11 is a schematic diagram of a facility including an
above-ground tank storage system.
FIG. 12 is a schematic diagram of a facility including a partially
above-ground tank storage system.
FIG. 13 is a schematic diagram of an underground storage tank
facility including a fuel access control unit.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
The method and apparatus described herein applies to UST's, AST's
or any type of storage tank. The product stored in the tank may be
any fluid, including dry particles that flow in the manner of a
fluid.
FIG. 1 shows a UST facility 10, illustrated as an automobile
service station. Facility 10 includes a series of UST's 12, 14, 16
which may store the same or different types of liquid fuel product
18. Volumetric tank gauges 20, 22, 24 in each tank measure the
height of product 18 in the tank. Submersible pumps 26, 28, 30 in
each tank pump product 18 to one of dispensing pumps 32, 34 through
piping lines 36, 38, 40. Alternately, facility 10 may be an AST
facility with above-ground tank 1000, as shown in FIG. 11, or a
facility with a partially above-ground tank 1010, as shown in FIG.
12.
Tank gauges 20, 22, 24 are mounted in tanks 12, 14, 16. The tank
gauges may consist of or be based on magnetostrictive tank probes
or other sensing technologies. In the case of magnetostrictive
technology, two floats 42, 44 surround each probe, e.g., gauge 20
in tank 12. One float 42 floats on the upper surface of product 18
in tank 12, and the other float 44 floats on the interface of
product 18 with any water or other foreign material collected at
the bottom of tank 12. Tank gauge 20 determines the distance
between floats 42, 44 to obtain the height of product 18 in tank
12. Tank gauge 20 also contains temperature sensors 46, 48, 50
spaced along its length to monitor the temperature of product 18 at
various depth levels.
Each of the dispensing pumps 32, 34 consists of a totalizer or flow
meter 52, 54 disposed in a housing 56, 58 to measure the volume of
product 18 dispensed through hoses 60, 62 and nozzles 64, 66. To
operate dispensing pump 32, nozzle 64 is removed from housing 56,
which actuates dispensing pump 32 and causes product 18 to flow
through hose 60 due to the pumping action of submersible pumps 26,
28, 30. A value stored in totalizer 52 is incremented as fuel is
dispensed through hose 60. Upon completion of the transaction,
nozzle 64 is replaced in housing 56, thereby turning off dispensing
pump 32 and discontinuing the action of submersible pumps 26, 28,
30 and totalizer 52.
Transactions are recorded electronically by software in a sales
recording device 71 connected to totalizers 52, 54 of dispensing
pumps 32, 34. Totalizers 52, 54 in dispensing pumps 32, 34 are
connected to sales recording device 71 by means of communications
and power supply wires 78, 80.
Sales recording device 71 contains software capable of emulating
the functions of a point of sale (POS) terminal associated with
fuel sales made at facility 10. POS emulation software in sales
recording device 71 functions on the basis of read only commands to
eliminate the possibility of conflict with control commands from a
POS terminal employed by facility 10. Alternative data acquisition
systems can result in destruction of credit card sales records,
inadvertently shutting down the entire system, and/or causing
electrical interference in the pump links.
Tank gauges 20, 22, 24 are connected to a tank monitor 82 by means
of communications and power supply wires 84, 86, 88 or communicate
data through radio frequency transmission. Tank monitor 82 converts
raw data obtained from tank gauges 20, 22, 24 into a form usable by
a computer.
A computer 70 contains a processor 72 capable of running various
computer software applications and a memory 74. Tank monitor 82 and
sales recording device 71 are electrically connected to computer 70
to relay totalizer values, product height and temperature data to
computer 70. Software executable by processor 72 of computer 70 is
capable of querying tank monitor 82 and sales recording device 71
to obtain measurement data at selected time intervals. The data is
continuously evaluated as it is collected and is stored in memory
74 of computer 70 for later retrieval and detailed analysis.
Alternatively, computer 70 may communicate with a host processor 90
at a remote location. The continuous evaluations or detailed
analysis may then be performed by host processor 90, which may be
faster or more efficient than computer 70.
As an example, computer 70 may be a personal computer or any other
proprietary microprocessor-based unit. Computer 70 may capture data
automatically through direct-connect serial interfaces with tank
monitor 82 and sales recording device 71, or by manual operator
keypad entry. Computer 70 communicates with equipment at facility
10 through four programmable serial communication ports, such as
RS-232 communication ports.
Computer 70 may, e.g., store tank dimensions and product
characteristics, and concurrent time and date data along with the
measurement data. Computer 70 may be used to produce error and
analysis reports as calculated by the software. It may also have
alarm event-initiated capabilities, such as when a leak is detected
in any of the tanks. Such a computer system can accommodate
facility and customer specific requirements while maintaining
complete compatibility with other system components.
The SIR method involves reconciling volume data obtained from tank
monitor 82 and volume data obtained from sales records. Sales
transactions may be detected in a number of ways, including an
electronic signal emitted from totalizers 52, 54, by voltage
sensing of control relays on pump dispensers 32, 34, or by
observation of product removal using tank gauges 20, 22, 24.
It is essential that the measurements used to obtain these two
types of data are made simultaneously. The SIR method of the
present invention collects and analyzes observations of sales
volumes and tank volumes which are derived simultaneously. Failure
to collect both types of data simultaneously would bias estimates
derived from separate volume measurements.
The SIR method properly accounts for the effects of temperature,
pressure and specific gravity. In addition, product from two or
more tanks may be blended, such as to achieve varying petroleum
octane levels at pump dispensers 32, 34. When different fluid
products are blended, the tanks are treated as one unit, and an
additional parameter is introduced to determine the actual blend
percentages.
Data concerning the physical characteristics of the tank
configurations and the accuracy of the various gauges and metering
devices is collected during installation and a set-up phase of
operation of facility 10 to create a basis for subsequent
statistical analysis. Information is then continuously collected so
that the statistical analysis of SIR can be performed by computer
70 or host processor 90.
Several procedures are used either singly or in combination to
obtain the volume observations. First, where the system
configuration provides for determining whether hoses and dispensers
associated with a given tank are active, the system is queried on a
minute-by-minute basis, or on the basis of another predetermined
time interval, to determine the status of the dispensers. When all
of the dispensers are idle, the values from totalizers 52, 54, the
tank volumes (i.e. product heights in the tanks) and temperatures
are recorded.
Second, submersible pumps 26, 28, 30 are checked to determine
on/off status. When it is determined that the pumps are turned off,
the values from totalizers 52, 54 are read, and tank volumes and
temperatures are recorded.
Third, software algorithms used by computer 70 detect and measure
leads and/or lags between the recording of sales events and
corresponding gauge and meter readings. When leads or lags are
encountered and constitute a physical characteristic of the data
measurement and recording system, constrained optimization, rather
than unconstrained optimization, may be used to determine parameter
estimates. Lagrange multipliers are one example of such a
constrained optimization method.
The method of the present invention is capable of providing dynamic
monitoring of system performance. For example, the leak detection
function is carried out continuously while normal operations, e.g.,
removals and deliveries, are taking place. To detect leaks
dynamically, the software is programmed to detect when sales or
delivery events occur and to calculate the volumes of product
removed or added as a result of such activities. Thus, dynamic
testing does not require that the system be dormant and addresses
the entire system from the point of filling to the point of
dispensing.
The SIR method of the present invention also distinguishes between
one-time removals and continuous losses consistent with leakage.
The integrity or leak-free status of the system is not assumed a
priori. Instead, the individual and unique characteristic pattern
induced by each form of error when viewed along the separate
dimensions of time, product height and sales volume are used to
identify and quantify the errors. The method may also be used to
detect and quantify undocumented removals, e.g., theft or additions
of product.
Further, the overall system is self diagnosing in that it
determines from the data the maximum degrees of reliability and
precision of which a particular operating configuration is capable
at any given time, as well as the degree of calibration
accuracy.
In particular, product height in the tanks and temperature are
measured continuously at, e.g., one-minute intervals. Height and
gross volumes are converted to net volumes at, e.g., 60.degree. F.
or 15.degree. C., using the algorithms described below. Sales
recorded by the totalizers 52, 54 are extracted and stored in
memory 74 at times coincident with readings from tank gauges 20,
22, 24. If the dispensing system is capable of transmitting a
signal indicating whether or not any or all individual hoses are
active, that information is also stored in memory 74 coincident
with taking gauge and meter readings.
The method of the present invention is designed to achieve the
maximum accuracy possible within the limitations imposed by the
inherent random and irreducible noise in the various measuring
devices incorporated. It utilizes multiple measurements over
extended time periods to identify and quantify systematic and
repeatable effects in the instrumentation and thereby correct for
such effects using the known physical characteristics of the
devices. The system makes no a priori assumptions as to the
accuracy of the devices used to measure product volume in the tank,
to measure volumes removed, or as to the accuracy of volumes
reported to have been delivered into the system.
The resulting volumetric calculations are independent of the
physical characteristics of the tank configuration and the various
measuring devices which may be incorporated in the system. The
results do not rely on input entered externally by the operator or
from diagnostics internal to the measuring devices used. Instead,
the output produced by the software which analyzes the measured
data depends only on the patterns induced in inventory data
produced by the tank gauges and measuring devices and, in
particular, the cumulative variances that result when the various
input values are combined.
Various error patterns which the measuring devices can induce and
the effects of temperature, tank geometry, and orientation on
cumulative variances are derived from empirical analysis of
real-world inventory data. The system's software synthesizes the
output measurements of the various devices based on known
characteristics derived from the empirical data. Thus, the software
is capable of identifying measurement errors caused by the
measuring devices and simultaneously compensating for the effects
of those errors.
Gauges can be systematically inaccurate in two ways. The height of
the product in the tank can be incorrect, and the height to volume
conversion algorithms may not reflect accurately the true
dimensions of the tank or its orientation in the ground. The latter
may be the result of incorrect measurements or an inappropriate
conversion algorithm.
The presence of such systematic effects and their nature may be
established by examining the pattern of inventory variances as a
function of product height. Errors of this kind induce patterns
which repeat themselves as the tank is filled and emptied. If the
tank length is incorrect, a linear pattern is induced. If product
height is in error, a curvilinear pattern results reflecting the
varying volumes in different cross sections of a cylindrical tank.
Tilt along the length of the tank induces a sinusoidal pattern
symmetrical about the mid-height of the tank. Absent such errors,
the pattern will be purely random, reflecting only the inherent
noise of the measuring devices. The absence of randomness and the
presence of a systematic pattern serves to identify the presence of
systematic error. The pattern of a departure from random and its
extent determines the source and extent of the systematic effects
and the means necessary to correct them.
Dispensing errors, unlike volume measuring errors, are independent
of product height, but are sensitive to the volume of product
dispensed. The nature and extent of dispensing errors can be
established by examining inventory variances as a function of sales
volume. As in the case of volume measurements, in the absence of
systematic errors, variances as a function of sales volume will be
random. The form and extent of departures from randomness serve to
determine the source and extent of the errors and provide for their
removal.
Leakage from the system creates a continuous downward trend in the
cumulative variance when viewed as a function of time. By contrast,
one-time additions and removals of product cause significant upward
or downward translations of the cumulative variance which remain
permanently in the record and do not introduce a continuous trend.
Leakage is distinguishable from tank gauging errors when viewed as
a function of product height because the pattern does not repeat as
the tank is filled and emptied. If product is leaking from the
system, a series of parallel translations in the cumulative
variance is generated, each shifted by the volume of product lost
between deliveries.
The accuracy of measurements taken from the various components of
the system determines the accuracy achievable in any one individual
observation. Since the leak rate is computed from a series of
successive observations, however, the minimum detectable leak rate
can be reduced to any desired magnitude by increasing the number of
successive observations recorded. Thus, the system can serve as a
final verification for leakage indications obtained by other
methods.
At the conclusion of an initial set up period of data collection
including one or more delivery and sales cycles, the collected
measurement data is analyzed by regression analysis. The initial
set-up regression is used to derive tank dimensions and
orientation, individual meter calibrations and secular trends. A
confidence level value p is computed at the 0.01 level of
significance to determine the minimum leak rate detectable by the
system, and the residual variance is computed to provide the
current noise level of the system.
The regression is performed according to the following equation:
##EQU1##
where: st.sub.i (R,L,T)=Volume in gallons derived from the ith
gauge reading in inches in a cylindrical tank with or without
hemispherical end caps with radius R, length L, and tilt over its
length of T inches. a=Initial inventory in gallons, which is to be
estimated. Sa.sub.kj =Sales volume recorded on the kth totalizer.
.alpha..sub.k =Fraction of sales volume recorded on the kth
totalizer actually removed from the tank, which is to be estimated.
D.sub.j =Volume of the jth delivery. Et.sub.i =Elapsed time since
initiation until the ith gauge reading is recorded. Ls=Constant
gain or loss in product per unit of time. B.sub.j =Volume of
product added (e.g. delivery) or removed during some discrete time
interval prior to or during observation period j. ##EQU2##
All of the parameters are estimated simultaneously using least
square estimation procedures. The R and T parameters are derived
numerically, but the other parameters are derived analytically.
Further, all of the parameters, including the initial inventory,
are estimated simultaneously. The initial volume must be estimated
from all succeeding data, even if the tank is initially empty,
otherwise the initial gauge reading and its conversion to gallons
is assigned a credibility not assumed for all succeeding readings.
Also, in a great majority of applications, the initial inventory in
an already existing and operating system is not accurately
known.
Initial inventory estimation is vital in determining the geometry
of the tank. When tank geometry, tank orientation, or tank product
height measurement depart from the values obtained from nominal
sources, all gauge and meter measurements are affected. It is
practically impossible to detect the errors induced in the gauge
measurements and correct for them unless the estimation of the
initial inventory is made coincident with the estimation of the
values of the other parameters.
The estimate of the parameters are based on the totality of the
data collected. This means, e.g., that the estimate of leak rate Ls
is determined from a linear trend including all of the data
collected, not merely at one end of the reconciliation period.
Likewise, estimates of tank dimensions and orientation are derived
from their overall contribution to reduction in residual variance,
as opposed to a sale by sale analysis of tank segments.
The volume st.sub.i (R,L,T) is derived from the product height
measurement by multiplying the constant area of tank segments of
height h (in inches) by tank length L. The volume in gallons of
product in a horizontal cylindrical tank of radius R is given by:
##EQU3##
In the case of a tilted tank, the area of the segments varies with
position along the length of the tilted tank, and the volume is
determined by integrating over the length L. Such integration does
not result in a closed form because the cross sections are not
circular, and a numerical integration would severely limit the
frequency of observations. Instead, in this application the tank is
treated as lying horizontally and the product is considered tilted,
to derive an equivalent volume. This integration yields the closed
form: ##EQU4##
The integrand is evaluated between the normalized product heights
in inches, hu/R and hl/R, at the lower and higher ends of the
tilted tank, respectively. It is standard industry practice to
install tanks on an incline to divert water and sludge away from
the submersible pumps.
Tank tilt is identified from the pattern it induces in the record
of cumulative variances as a function of product height. It is
compensated for by fitting the correct mathematical form for height
to volume conversions in a tilted tank to the cumulative variance
calculated by the method of least squares. This is done
simultaneously with estimation of the initial inventory.
Tank length L and radius R are established by equating the first
partial derivatives of the sum of squared cumulative variance with
respect to length and radius and determining the values which
minimize the sum of squared variances. Simultaneous estimation of
initial inventory is also required when estimating tank length L
and radius R.
Errors in measurement of the product height h in the tank are
characterized by curvilinear patterns induced by height to volume
conversions in the cumulative variance for a cylindrical container
when heights are transposed upward or downward. Such errors also
are compensated for by minimizing the sum of squared cumulative
variances with respect to increments or decrements to measured
product height. This estimation also requires simultaneous
estimation of the initial inventory of the tank.
In general, the accuracy of the estimates of the tank dimensions,
tank orientation and height measurements is confirmed by observing
that the cumulative variances of each derived value as a function
of nominal product height are random and display no systematic
influence or effects.
Dispenser totalizer calibration is continuously monitored and
evaluated by minimizing the sum of squared cumulative variances
with respect to multiplicative constants associated with individual
reported cumulative sales volumes from all pump dispensers
associated with a particular tank system. This eliminates the need
for manual verification of meter calibration.
In particular, gauge performance is continuously monitored to
identify gauge malfunctions or degradation in gauge performance.
Monitoring of gauge performance is independent of diagnostics which
are internal to the measuring device. Diagnoses of problems are
based only on their impact on the cumulative inventory variances
which are continuously monitored by the software.
If the gauge fails to record changes in product height when the
dispensers register sales, an increase in cumulative variances
approximately equal to sales volume is observed; this effect can be
identified by the monitoring software and a warning of gauge
malfunction generated to the operator.
However, observation of the gauge registering product height
change, but with a time lag after sales are recorded, may be a
feature of normal gauge performance. Such normal gauge performance
is identified by repeated positive increments in cumulative
variances as sales are completed with subsequent return of the
cumulative variance to normal bounds. When such gauge function is
determined to be the normal operating characteristic of a
particular system, constrained optimization with lagged variables
is introduced into the software. Otherwise, the gauge's performance
is reported as a malfunction.
Finally, temperatures in the tank are monitored to detect changes
that are excessive for the time intervals between observations.
Erratic temperature readings are deleted, and may indicate gauge
malfunction.
The software computes actual, rather than nominal, delivered
quantities and requires no input by the system operator. The
operator may choose to input into the system the nominal delivery
quantity indicated by the delivery invoice, along with the
temperature and coefficient of expansion of the product at the
point of pick-up. The software will then compute overages or
shortages between the nominal and actual quantities delivered, as
well as the overages or shortages caused by temperature-induced
variations in the transport of the product to the facility and in
the subsequent mixing of the delivered product with that resident
in the tank.
Delivery is identified by the software when a positive cumulative
variance is observed which exceeds the system noise level and is
not succeeded by a return to normal variance bounds. Delivered
quantities are computed by estimating the volume increases they
induce in multiple, successive observations. The required number of
successive observations is determined as that sufficient to
generate a confidence width which is within a predetermined
tolerance. The system of the present invention is capable of
accounting for sales conducted during delivery and for noise
introduced by post delivery turbulence in the tank.
One-time unaccounted for removals or additions to the tank are
computed in the same manner. Deliveries are distinguished from such
events by computing the rate of input, which in the case of normal
gravity delivery should exceed 100 gallons per minute. Other modes
of delivery, e.g. pipeline delivery into above ground tanks, are
identified by incorporating their known delivery rates.
Leakage from the system is identified by a continuous linear
negative trend in the data which exceeds the computed minimum
detectable leak rate after all of the various error phenomena
described above have been identified and compensated for. This
calculation deals with the totality of the data obtained by
constantly monitoring known removals and is not restricted to
observations made only when the system is dormant. It is also
independent of any single data reconciliation calculation in that
trends throughout all of the data are evaluated.
All calculations concerning volumes are made on the basis of net
volumes, according to the following definitions:
where: t=Measured temperature in degrees Fahrenheit (if centigrade,
the term in parentheses becomes (t-15)). CE=Coefficient of
expansion.
and ##EQU5##
where t.sub.1 and t.sub.2 are temperatures measured by the tank
gauge at the beginning and ending of a sale transaction,
respectively. Deliveries are computed in net gallons, but are
converted to gross quantities if required, based on external
information input by the system operator, as follows: GT=Gross
gallons on invoice at the originating terminal. NT=Net gallons on
invoice at the terminal. tT=Temperature at the terminal.
CE=Coefficient of expansion.
The program also records: tA=Ambient temperature in the tank prior
to delivery. tF=Temperature in the tank at the conclusion of
delivery.
The following value is computed: ##EQU6##
where:
NVD = Actual net volume delivered, previously computed. NVA = Net
volume in the storage tank at the start of delivery. NS = Net
overage (+) (underage (-)) in delivery. = NT - NVD GVD = Gross
volume delivered. = NVD (1 + (tF - 60) CE) GVS = Gross volume in
the transport vehicle at the facility prior to delivery. = NVD (1 +
(tS - 60) CE) GSM = Shrinkage due to mixing in the tank. = GVS -
GVD GVT = Actual gross volume in the transport vehicle at the
facility. NVD (1 + (tT - 60) CE) GST = Shrinkage during transit to
the facility. GVT - GVS GOS = Gross overage (+) (underage (-))
adjusted for temperature effects. = GT - GVD + GST + GSM
Calculations of volumes actually delivered are based on multiple
observations of the balance of measured tank volumes and cumulative
sales. This method requires frequent simultaneous observations of
sales and in-tank volumes (i.e. product heights) and
temperatures.
The volume of product in a tank is derived by measuring the height
of the product and using the geometry of the tank, which is assumed
to be known, to compute the corresponding volume. In many
instances, tank dimensions vary substantially from assumed design
dimensions. Regulatory specifications permit up to 10% variation in
length and diameter of cylindrical tanks.
Tank orientation can also cause complications in the calculations.
The volume corresponding to a measured height varies substantially
when the tank is tilted away from horizontal or rolled away from
vertical.
Further, tanks may also fail to conform to a known geometry either
through faulty manufacture or installation, or may suffer
significant deformation during the course of operations. For
example, many fiberglass tanks sag or bend along their length.
In addition, installed tanks are typically inaccessible, and
difficult to measure. Thus, it is necessary to confirm the accuracy
of height to volume conversions from generated inventory data and
to identify and correct discrepancies where they exist.
The foregoing problems are compounded when two or more tanks are
manifolded together. Manifolded tanks are joined together by piping
systems and serve common dispensers. Thus, sales quantities from
manifolded tanks constitute withdrawals from all tanks in the
manifolded system, but not necessarily in equal quantities. Product
heights typically vary from tank to tank, but tank geometries,
dimensions and orientation may also vary so that a procedure for
correcting height to volume conversion errors for a single tank
will not apply.
The different factors which influence inventory data manifest
themselves in distinct ways which facilitate their identification
and correction. These factors are most easily identified by
examination of their effects on cumulative departures of actual
measured inventory from a theoretical or book value when viewed
across a variety of dimensions. In particular, one-time
undocumented physical additions or removals of product, e.g. over
or under deliveries and pilferage, are evidenced by an addition or
subtraction of a constant quantity from the cumulative variance at
the time of occurrence and all subsequent observations. Continuous
loss of product accumulating over time, e.g. leakage, is evidenced
by a loss trend over time. Continuous loss of product varying
proportionally with sales value, such a line leak or meter
miscalibration, may be determined by identifying a constant
negative trend that is cumulative only over periods where delivery
lines are pressurized.
A pattern of gains or losses, or both, recurring cyclically as the
tank is successively filled and emptied with no long term gain or
loss of product, is the pattern associated with height to volume
conversion error. The pattern is cyclical because the error source
is identical in each cycle as the tank is filled or emptied. It is
distinguishable from the other patterns in that it retraces the
same path without the translation which would occur if physical
loss or gain of product were taking place.
This problem is most readily diagnosed by analyzing cumulative
variance as a function of product height. If the variances are
random with no evidence of systematic effects, height to volume
conversions may be assumed to be correct. If not, the form of the
induced pattern indicates the nature of the conversion error. Thus,
an error in tank length induces a linear pattern, an error in tank
tilt induces a sinusoidal pattern, and a constant error in tank
height measurement induces an arc-like pattern. When other sources
of loss or gain are present, the conversion error patterns remain,
but are translated in each succeeding filling/emptying cycle to
reflect the physical loss of product which has occurred during that
cycle. Thus, confusion between conversion errors and other effects
can be eliminated.
Sales readings and product height measurements must be made
simultaneously. Since the number of observations in any one sales
cycle is typically too few to generate a conversion table of
sufficient detail to be of practical use, subsequent sales cycles
and their corresponding deliveries must be incorporated. If,
however, deliveries are unmetered and are used to approximate the
volume (as is the standard industry practice), significant
inconsistencies are introduced. If an overage or shortage occurs
during delivery, then all subsequent sales volumes correspond to
tank cross sections which have been shifted upward or downward from
their predecessors. Averaging or statistical treatment cannot
overcome this deficiency since there is no means of knowing without
metering whether, by how much, and in what direction the data has
been shifted.
The procedure of the present invention may include determining if
height to volume conversion error is a problem. If the error is a
problem, then the system must determine the nature of the problem,
e.g. tank dimensions, tank orientation, height measurement or
unknown tank geometry, and whether the conversion problem is
compounded by other gains and losses. If leakage is suspected, an
on-site leak detection investigation is undertaken. In no leakage
is indicated, and one or all of tank dimensions, tank orientation
and height measurement are problems, new conversion factors are
calculated and confirmed using the diagnostic procedures described
herein.
If unknown tank geometry or manifolded systems are encountered, the
exact current percentage of metered sales actually dispensed from
each dispenser is determined by physical measurement. A high order
polynomial using a variable of measured product height is used to
convert height to volume. The parameters of the polynomial are
derived from the differences between measured product height
corresponding to the beginning and ending of sales events which do
not overlap deliveries.
For a single tank, actual dispensed quantities are regressed using
a polynomial based on the differences in measured product height
before and after individual sales, subject to the constraint that
when the polynomial is evaluated at a height equal to tank
diameter, the result is the total tank volume. Observations which
include delivery events are discarded.
A fifth order polynomial has proven adequate in most cases.
Residual analysis may be used to determine adequacy of the
polynomial in the presence of severe tank distortions, and higher
order polynomials may be introduced as necessary. The number of
observations required is determined by estimating a confidence
bound around the resulting polynomial with a width adequate for the
desired resolution. Thus, ASale.sub.i =Actual dispensed volume in
period i. h.sub.i =Product height upon conclusion of ASale.sub.i.
h.sub.i-1 =Product height prior to commencement of ASale.sub.i and
after completion of ASale.sub.i-1. d=Diameter of tank. Vol=Total
volume of tank.
The converted volume for height h is then given by:
The omission of a constant term in the regression implies that
This ensures that the polynomial derived from the height
differences is well defined.
For manifolded systems, actual sales are regressed simultaneously
on individual polynomials based on the various height differences
in the several tanks which correspond to a particular sales volume,
subject to the constraint that each polynomial evaluated at the
corresponding tank diameter yields the total volume of that
tank.
where: ASale.sub.i =Actual Sales volume in period i. h.sub.i-1j
=Height of product in tank j after completion of Asale.sub.i-1 and
prior to commencing Asale.sub.i. j=1, 2, . . . m h.sub.ij =Height
of product in tank j after completion of ASale.sub.i. m=Number of
tanks manifolded. Volume conversion for the m measured heights,
h.sub.1, h.sub.2, . . . h.sub.m in the total system is:
##EQU7##
where: h.sub.i =Height of product measured in the ith tank in the
manifold.
Delivery inaccuracies have no impact on this calculation since all
observations made during deliveries are discarded. Height changes
are related only to the corresponding volumes dispensed.
Prior determination of actual quantities dispensed, as opposed to
metered quantities, ensures that the only remaining source of error
is random measurement error. Regression is designed to accommodate
random error of this kind and to facilitate inferences when errors
are present.
An alternative method of estimating volume of product based on
product height in single or manifolded tanks involves determining a
volume function by integrating a differential of the volume
function. The total differential of the volume function is
estimated using one of several procedures, e.g., least squares
estimation. For example, for a manifolded system of storage tanks,
if
where Sa.sub.i is the measured volume change associated with
measured changes in product height during a dispensing event from
the manifolded tanks, then
where V.sub.j (h.sub.1 i, . . . ,h.sub.m i) is the partial
derivative of the volume function with respect to h.sub.j, the
height of the fluid in the j.sup.th tank. The least squares
technique provides maximum likelihood estimates because measurement
errors occurring in tank gauges 20, 22, 24 have been established to
be normally distributed.
A differential function for a volume function having any functional
form may be estimated in this manner. For example, a high order
polynomial may be used and constrained to have a preset volume at a
maximum height, zero volume for zero height in all tanks and/or
zero value of the first derivative at maximum height and at zero
height.
For example, if h.sub.ij =product height in tank i at the
completion of sale j and prior to the start of sale j+1, Sa.sub.k
=volume dispensed in sale k, and the volume function is an r.sup.th
order polynomial in the form ##EQU8##
then ##EQU9##
where the linear term of the polynomial is omitted to provide a
zero derivative at h=0. Then, the following equation may be
minimized ##EQU10##
subject to ##EQU11##
and ##EQU12##
for i=1, 2, . . . ,m where hmax.sub.i is the maximum product height
in tank i, Volmax.sub.i is the preset maximum volume in tank i, and
m is the number of tanks in the manifolded system.
The foregoing equation works well for m=1. For m>1, a further
constraint is required to ensure upward concavity of the individual
volume functions near zero volume. This is accomplished by
constraining the second partial derivatives of the individual
volume functions to be positive at zero volume. In the case of
polynomial functions and tanks with equal radii, this reduces to
the constraint a.sub.1 1 =a.sub.2 1 = . . . =a.sub.m 1.
Alternatively, the volume function may take the form ##EQU13##
where f(h) is a function of the height h. The derivative of
##EQU14##
Numerical minimization may be used to estimate this derivative
function. An advantage of a function in the form of V'(h) is that
it asymptotically approaches zero (0) near h=0 and one (1) near the
maximum height.
Determining the volume function by integrating an estimated
derivative of the volume function has many advantages. For example,
the data used to estimate the derivative consists of discrete
measurements of dispensed volumes and corresponding product height
changes, which avoids introducing ambiguities and errors due to
inaccurate calculations of deliveries of the product. The data does
not need to be sequential, and data for periods during deliveries
and post delivery turbulence may be discarded. Because the only
error sources are in the metering devices (for which calibration
may be determined as described herein) and random errors of height
measurement (the magnitude of which may be determined as described
herein) the error resulting from the height to volume conversion
may be contained within acceptable limits. Further, the volume
function derivative may be estimated accurately because the system
can collect a large number of data points, which may be stored in a
compressed format as described below, and because the system avoids
delivery calculation errors. As in the case of calculating the
volume of product in a single tank, the sales, volume and tank
height measurements must take place simultaneously, the calibration
of individual meters must be monitored and recorded, and a large
volume of data must be collected and recorded.
With respect to temperature, the temperature of product delivered
into a tank system almost invariably differs from the temperature
of the product already in the tank. Its addition has the effect of
expanding or contracting the volume of the combined product. This
change in volume can create the appearance of incorrect dimensions
of the height to volume conversion, appear as leakage where none
exists, or it can mask the existence of actual leakage.
It is therefore preferable, and frequently essential, that all
volumes, sales, deliveries and product in storage be converted to a
common temperature prior to analysis. Typically 60.degree. F.
(15.degree. C.) is chosen as the standard. The conversion is
accomplished as follows:
where: t=Measured product temperature in degrees Fahrenheit.
CE=Coefficient of expansion.
As above, all calculations are in net gallons of product.
A complication to the calculation may occur if the tank gauges 20,
22, 24 used to measure product volume are designed for static or
dormant mode tank testing. Such tank gauges detect leakage when the
tank is taken out of service. In this case, product volume changes
due to temperature changes during the course of a test must be
accounted for.
Further, as shown in FIG. 1, temperature sensors 46, 48, 50 are
located at different heights in tank 12. If the level of product
falls below a given temperature sensor, the corresponding weighted
temperature measurement is dropped from the average temperature
calculation, and a temperature jump and corresponding volume change
may be observed when the net volume is calculated using the new
weighted average of temperatures. If uncorrected, such repeated
jumps in the data would preclude further analysis of the data for
leak detection or the generation of height to volume
conversions.
The system of the present invention may be used to overcome these
temperature related problems. Using the following definition,
NDB.sub.N =The net cumulative variance in the inventory data at
observation N.
then, ##EQU15##
where: a=Gross initial inventory. t.sub.0 =Temperature of initial
product volume. t.sub.i =Temperature of product at observation i.
Sa.sub.i =Gross volume sold in period i. V.sub.N =Measured gross
volume in tank at period N. CE=Coefficient of expansion.
Absent random error or leakage, and assuming no deliveries of
product, then
and ##EQU16##
Therefore, if a temperature jump to temperature t* occurs at an
observation N+1, then ##EQU17##
When this final quantity NDB.sub.N+1 is added to the volume where
the transition occurs between temperature sensors, and all
subsequent volumes, the effect of the transition is eliminated, and
analysis proceeds as it would where individual temperature readings
are available.
A large number of variables must be estimated by the software to
implement the SIR system of the present invention. For example, as
many as forty hoses and independent totalizers per tank system, as
well as deliveries numbering four or more per day must be
accommodated. Thus, a very large volume of data must be
accumulated, encompassing a substantial spread of sales volumes
from each totalizer for both the set-up analysis and subsequent
routine monitoring. To accommodate this volume of data within
current or conceivable future practical computer memory
capabilities, the algorithm implemented by the software utilizes a
matrix formulation which invokes the property of a sufficient
statistic to reduce the memory requirement.
The calculations used to determine the various error, loss trend
and delivery estimates have the form:
##EQU18## S.sup.2 =(x.sup.T x).sup.-1 MSE
where: B=Column vector of m parameters to be estimated. x=Matrix of
parameter coefficients. y=Column vector of independent variables.
MSE=Mean squared error. S.sup.2 =Variance covariance matrix of
parameter estimates.
The values contained in vector y comprise tank gauge readings. The
entries in matrix x are measured sales volumes, time, and other
constant values. The parameters of vector B which are to be
evaluated include the initial volume of the system and subsequent
volume changes, including delivery amounts.
For example, if observations are recorded every minute, as many as
1440 rows in the x matrix and the y vector may be recorded. It
would clearly be impractical to accumulate and store data in that
form over an extended period of time. Instead, data compression
techniques are applied so that only a manageable amount of data
need be stored.
The algorithm utilizes the property that if an n.times.m matrix A
is partitioned into two submatrices, B and C, where B is an
i.times.m matrix and C is a j.times.m matrix, such that i+j=n,
then
For example, ##EQU19##
At the conclusion of each 24 hour or other period, only x.sup.T x
and x.sup.T y are computed and stored. The matrix x has the form of
a square n.times.n matrix. Further, the aggregates of observations
for different periods are additive, since two square matrices
having n.times.n dimensions may be added. Thus the total data
storage requirement for each period is determined only by the
square of the number of parameters of interest.
The system is able to accommodate virtually unlimited numbers of
observations by this method of data compression. Without this
capability, the system would not have the storage capacity to
accurately and simultaneously estimate the numbers of parameters
which are required to perform a statistically significant
calculation. This data compression method also allows for
processing the data at the facility or for transmitting the data to
a host computer for periodic analysis. FIGS. 2, 3 and 4 show the
Mathcad computer code used to perform the data compression
algorithm.
Furthermore, (x.sup.T x).sup.-1 x.sup.T y is a complete and
sufficient statistic for B. No statistically useful information is
lost in the compression. The overall procedure is, therefore,
unlimited by memory. The only limitation remaining is the precision
available in the computer system used.
The software performs SIR analysis, including inventory estimation
and leak detection, using the above equation in the following
form:
where: range=1 . . . (number of observations) meters=1 . . .
(number of dispensers)
and 1.sub.range =Column of 1's. T.sub.range =Cumulative time in
minutes. (S.sub.range).sup.<meters> =Cumulative sales for an
individual dispenser in gallons. CD.sub.range =Cumulative
deliveries. Stk.sub.range =Tank stick reading in gallons.
To estimate the initial inventory, the matrix x includes a column
of unitary values. To estimate loss trends, the matrix x includes a
column containing cumulative times of measurement and cumulative
sales. The values of B, MSE and S.sup.2 are then calculated,
producing the following result for the vector B: B.sub.1 =Estimated
initial inventory. B.sub.2 =Loss trend. B.sub.2+meters =Individual
meter error.
B is the vector containing the parameter estimates, namely
beginning inventory, meter calibrations and loss rate. The loss
rate estimate is in the second row (n=2). S.sup.2 is the variance
covariance matrix of the parameter estimates. Thus, S.sub.22
=(S.sup.2.sub.22).sup.1/2 is the standard deviation of the loss
rate estimate. Finally, the minimal detectable leak is defined as
t.sub..alpha. S.sub.22, where t.sub..alpha. is the (1-.alpha.)
percentile of the Student's t distribution.
The software performs delivery calculations using the equation in
the following form:
where: range=1 . . . (number of records)
and 1.sub.range =Column of 1's. T.sub.range =Cumulative time in
minutes. S.sub.range =Cumulative sales in gallons. D.sub.range =0
where T.sub.range is less than delivery time and 1 where
T.sub.range is greater than or equal to delivery time.
Stk.sub.range =Tank stick reading in gallons.
The values of B, MSE and S.sup.2 are then calculated, producing the
following result for the vector B: B.sub.1 =Estimated initial
inventory. B.sub.2 =Loss trend. B.sub.3 =Meter error. B.sub.4
=Estimated delivery amount. S.sup.2 is the variance covariance
matrix of the estimates. Thus, S.sub.44 =(S.sup.2.sub.44).sup.1/2
is the standard deviation of B.sub.4, the delivery volume estimate.
The delivery tolerance is B.sub.4.+-.t.sub..alpha. S.sub.44, where
t.sub..alpha. is the (1-.alpha.) percentile of the Student's t
distribution. Delivery tolerances can be reduced to any desired
value by increasing the number of observations used in the
calculation.
The SIR analysis used by the method of the present invention
involves computing and comparing cumulative variances. When the
initial set-up is complete, computed trend and meter calibrations
are used to project forward an expected cumulative variance, that
is, the expected value of the difference between gauge readings and
computed inventory. Actual cumulative variances are then computed
from all subsequent gauge and meter readings and compared to the
expected variance.
FIGS. 5, 6 and 7 show the routine operation procedure 100 followed
by the software to perform this analysis. Data from the set-up of
the system and the most recent analysis is entered into the program
at step 102. The data entered includes the tank type, tank
dimensions, tank tilt, meter calibrations, mean square error and
calculated trends. At step 104, three variables established as
counters, Counter1, Counter2, and Counter3, are set at zero. The
measurement data from the system itself is entered at step 106,
namely the readings from the dispenser totalizers, the product
height and the product temperature.
The software computes the gross volume of the product, the most
recent gross volume and the sales as measured by the individual
dispensers at step 108. The software further manipulates the data
at step 110 by converting all gross volumes to net volumes,
computing observation to observation variance, and computing
cumulative variance. The sign of the cumulative variance is
recorded at step 112.
The program proceeds on the basis of the cumulative variance and
the value of Counter1 in steps 114, 120, 124, 128, 132, 136 and
140. Depending on the cumulative variance and the value of
Counter1, the program analyzes the collected data at step 118 if it
is a final observation (step 116), deletes the collected data
(steps 122 and 134), performs the analysis for a delivery (step
126) (see below), or reads new data (steps 116, 122, 130, 134, 138
and 142) upon updating the value of Counter1 and other
computational variables (i.e. index, sign index and sign). In some
cases, collected data is deleted (steps 122 and 134).
Upon computing the loss rate at step 144, the program reads new
data at step 146 if the loss rate is not greater than or equal to,
e.g., 0.2 gallon per hour, otherwise it computes the trend of the
data at step 148. If at step 150 it is determined that the trend is
greater than 0.2 gallon per hour, a warning is issued at step 156.
In either case, the software continues to read and analyze the data
at steps 152, 154, 158 and 160 until the last observation.
The operation of deleting data 170 is shown in detail in FIG. 8.
After performing similar analyses at steps 172, 174, 176, 180 and
184, using the indices and the values of the calculated standard
deviations as in the routine operation procedure described above,
the values of the counters are updated and new data is read at
steps 178, 182, 186 and 188. Data is deleted in accordance with
steps 178, 186 and 188.
Finally, FIG. 9 shows the delivery calculation 190 in detail. After
determining that the cumulative variance is greater than a
predetermined value (three standard deviations) at step 192, the
program determines whether the variance is greater than, e.g., 100
gallons per minute (step 194). If so, the delivery is recorded and
the amount delivered is determined at steps 202 through 220.
If there is a delivery in progress (step 202), data is read until a
negative observation to observation variance is observed (step
204). The variance is monitored until the turbulence in the tank
subsides (step 206). Thirty observations are read (step 208), and
all observations from 15 minutes before the delivery until the end
of the turbulence observations are deleted (step 210). An indicator
variable is introduced with the turbulence observation, from which
regression commences (step 212). The confidence bound on the
indicator is computed (step 214). If the confidence bound is within
a predetermined tolerance, the volume of the delivery is reported
within the confidence bounds (step 220); otherwise, additional
observations are added, and the confidence bound is recomputed
(step 218).
If the variance between data measurements is less than 100 gallons
per minute, the software determines whether the gauges are
inoperative and reports them as being inoperative (step 198), or
proceeds as in the routine operation procedure according to step
200 (in which there is a negative variance) depending on whether
the observed variance exceeds a predetermined value (within one
standard deviation) at step 196.
In general, if observed variances are within three standard
deviations or other predetermined tolerance of the expected value,
the data is stored for future analysis. When cumulative variance
exceeds three standard deviations or other predetermined tolerance,
different software programs are executed depending on the nature
and magnitude of the departure.
If within ten (or other predetermined) successive observations
after the initial departure, the cumulative variance returns to
within the tolerance range, all data from and including the initial
departure and prior to the initial observation are deleted. The
time extent and number of observations involved is recorded and
stored for, e.g., a daily gauge performance report.
If all ten (or other predetermined) successive observations remain
outside the tolerance bound and the cumulative variances are of the
same sign, a new trend line is initiated at the point of initial
departure. After ten (or other predetermined) additional
observations, a third trend line is initiated. If the increment to
the overall trend estimated from the most recent observations is
not significant, the most recent data is consolidated with the
previous data and the process is repeated until such time, if ever,
that the current trend increment is significant.
If the departure is positive, the system checks whether the product
is being dispensed and whether the gauge height fails to decrease,
reflecting removal from tank. If so, the tank gauge is reported to
be inoperative.
If the gauge height is increasing, monitoring is continued as above
until the most recent trend line returns to its original slope.
Minute to minute variances are monitored to detect turbulence until
the gauge values again return to within tolerance. All observations
which occurred in the fifteen minutes prior to the first positive
departure until the end of post delivery turbulence are deleted. An
indicator variable is introduced at the first observation after
post delivery turbulence. The system collects thirty additional
observations and performs the regression from the beginning of the
period to determine the volume delivered. The volume delivered is
then reported.
If the departure is negative, the system proceeds as with delivery.
If successive slope increments fail to show a return to the
original slope, indicating continuing loss of product for a
predetermined period, typically one hour, and the slope exceeds 0.2
gallon per hour, the system reports a warning that there is a
continuous loss of product. If the loss rate is less than 0.2
gallon per hour but greater than the minimum detectable leak, the
system continues to monitor and recalculate the parameters, to be
included in a daily operational report. If the incremental trend
line shows a return to the original trend, the system proceeds as
with delivery, introduces an indicator variable, deletes data as
necessary, and performs the regression to determine the volume of
product removed. The system reports a one-time removal of
product.
Referring to FIG. 10, the invention incorporates a data acquisition
and transmission network (DAT network) 300 to completely automate
the process of obtaining, capturing, transferring and processing
product inventory data for use in product management, delivery
scheduling and environmental compliance practices. DAT network 300
includes on-site processors 302, 304 at the facilities 306, 308
where the tanks are located, a customer host processor 310 and a
central host processor 312. DAT network 300 links multiple remote
facilities 306, 308 to central host processor 312, which performs
the SIR analysis. The link may be accomplished indirectly through
customer host processor 310, which itself is connected to a
plurality of remote facilities 306, 308. Each of these processor
elements is composed of independently operating software and
hardware systems which form the basis of a wide area network linked
by modems which transmit information electronically via the
telephone network 314 using standard dial-up voice grade telephone
lines. Examples of DAT networks are the TeleSIRA and ECCOSIRA
systems developed by Warren Rogers Associates, Inc., Middletown,
R.I.
DAT network 300 provides a uniform method of integrated management
for the widest possible variation of underground and above-ground
fuel storage, movement and measurement systems. On-site processors
302, 304 are capable of obtaining information from any electronic
or mechanical control system, enabling DAT network 300 to
accommodate facility configurations that are unique to each
facility while presenting the information captured at remote
facilities 306, 308 to customer host processor 310 or central host
processor 312 in a uniform format.
On-line processors 302, 304 obtain and capture product inventory
data through the use of proprietary interfaces with external
systems in use at remote facility 306, 308, such as tank gauges and
sales recording devices. On-line processors 302, 304 transfer
captured information daily, weekly or monthly through the public
switched telephone network 314 to customer host processor 310 or
central host processor 312 for use in inventory management,
delivery scheduling and/or environmental compliance. On-site
processors 302, 304 may be, e.g., touch-tone telephones acting as
sending units and Windows-based multi-line, voice prompt/response
PC's as the receiving units. On-site processors 302, 304 may be
designed to meet the specific needs of facilities 306, 308 without
requiring remote hardware at the facility in addition to that
already present.
In particular, each of on-site processors 302, 304 typically is
equipped with an alphanumeric keypad, a character display, a power
supply, four programmable serial communication ports, an internal
auto-dial/auto-answer (AD/AA) modem and a local printer port (for
connection to a printer). The keypad and display allow for operator
configuration and manual entry of sales, delivery and tank level
data. Use of an AD/AA 2400 baud modem allows multiple on-site
processor 302, 304 to share an existing voice grade telephone line
by establishing communication windows to minimize attempted
simultaneous use. Each of the programmable serial communication
ports is independent, fully programmable and governed by options
selected at the facility or off-site through modem access. Finally,
on-site processors 302, 304 can prompt the facility operator to
enter missing or suspect entries when results are outside the
expected range.
The use of customer host processor 310, which is capable of
receiving, storing and processing information from multiple on-site
processors 302, 304, enables the management of a remote tank
population from a single point of contact. A database of
information created by customer host processor 310 is the basis for
all higher level product management functions performed by DAT
network 300. The database is also the basis for the environmental
compliance analysis performed by central host processor 312.
The use of central host processor 312, which is capable of
receiving, storing and processing the information in the database
created by customer host processor 310 for product management
enables DAT network 300 to achieve maximum results by utilizing the
database for environmental compliance without additional remote
facility information or communication. Central host processor 312
is capable of transmitting a resulting database of the
environmental analysis back to customer host processor 310 for
printing and other customer record-keeping requirements.
The processor elements of DAT network 300 may exhibit other useful
operational characteristics. To prevent unauthorized access to DAT
network 300, a security access code for dial-up data transfer
functions is required. Under secured access, the baud rate, parity,
stop bit parameters and communication protocol are determined at
any of on-site processors 302, 304, customer host processor 310 or
central host processor 312.
Another function of DAT network 300 is to monitor tank contents
generally. DAT network 300 can be programmed to activate, e.g., an
audible and visual alarm if the water level in the tank is too high
(e.g., greater than 2 inches), if the product level in the tank is
too high (e.g., more than 90% of tank capacity) or too low (e.g.,
less than 10% of tank capacity, more product must be reordered, or
less than two days supply), and if a theft occurs (product level
changes during quiet periods).
The system may be used to obtain valuable information other than
inventory regulation and leak detection. For example, the system
may incorporate time series analysis routines, including Box
Jenkins, moving average and exponential smoothing, to derive
estimates of demand for the product which also incorporate temporal
and seasonal trends and special events.
The demand analysis may also be combined with additional inputs of
holding costs, reorder costs, transportation costs and penalty
costs for running out of stock. The system can include optimal
inventory algorithms to determine optimal order quantities, reorder
points and optimal delivery truck routing. Further, the system may
incorporate multiechelon, optimal inventory procedures to
accommodate combined wholesale and retail operations, such as with
calculus-based optimization and linear, nonlinear and dynamic
programming.
As shown in FIG. 13, a DAT network may include a fuel access
control unit or system 510 at a storage tank facility 500 such as a
UST automobile fueling facility. Fuel access control unit 510 is a
dispensing system actuated by the use of a device coded with
information, e.g., a card 520 with a coded magnetic stripe 522,
e.g, an optical punched card, an electrically erasable programmable
read-5 only memory (EEPROM) key, a radio frequency identification
(RFID) tag, a magnetic resonance coupler, a bar code, or other type
of coded medium which contains identification information
pertaining to the user. Fuel access control unit 510 may include
apparatus for a user to input information, e.g., a card reader 512,
a display 514, and a keypad 516, a control system 562 for turning a
fueling dispenser 560 on and off, and a processor 564 or similar
computing platform for controlling and monitoring the user's
fueling process. Manufacturers of fuel access control system which
rely upon optical reading devices or magnetic stripe card reading
devices to identify the user include FillRite, Fuel Master, Gasboy,
PetroVend and Trak Engineering.
Fuel access control unit 510 is used to monitor the activity of
fueling dispenser 560. Fueling dispenser 560 includes a hose 566
for dispensing fuel from a tank 515, a totalizer 568 and a meter
569 for measuring the volume of fuel dispensed by hose 566. Fuel
access control unit 510 may communicate with an on-site processor
530 located inside facility building 540 over a local area network
(LAN). The communications between fuel access control unit 510 and
on-site processor 530 may be over RS-232/RS-485/RS-485 (MultiDrop)
cabling 542.
Fuel access control unit 510 provides a system of controlling
access to fueling facility 500 by determining the identity of each
user of the facility and screening each user based on his or her
authority to purchase fuel. Identification of the user is made by
requiring the user to present a valid magnetic card (e.g., card
520), an optical punched card, an EEPROM key, an RFID tag, a
magnetic resonance coupler, a bar code, or other type of coded
medium, which contains identification information pertaining to the
user. Such fuel access control systems are referred to as island
control units or cardlock system. Further, the user may be required
to present additional identifying data by other means such as
buttons, key switches, or by entering information on keypad 516.
Once the identification data is collected, fuel access control unit
510 determines the user's fueling privileges, and based on this
information will either allow or deny fueling by the user. If
fueling is allowed, fuel access control unit 510 will enable
dispensing pump 560 for that particular user and monitor the
fueling process. At the completion of the fueling process, fuel
access control unit 510 will record the amount of the fueling
transaction in a memory 567 and retain the recorded information for
further accounting of the transaction.
Fuel access control unit 510 may be used to perform a variety of
functions, including the following: 1. Identifying the user by
reading a card or other coded medium and collecting the user's
identification information such as a driver license number or other
personal data. 2. Collecting other pertinent data for analysis,
such as an identification of the user's vehicle, the vehicle's
odometer reading, a trip number, the trailer hub counter, the
engine hour reading and/or a refrigerator unit hour reading. 3.
Making authorization decisions, to determine whether the identified
user is permitted to obtain fuel. 4. Enabling fueling by enabling
the proper dispensing pump for the user. 5. Monitoring fueling by
controlling the maximum amount dispensed. 6. Turning off the
dispensing system if no fuel is dispensed for a predetermined
period of time. 7. Recording the fueling transaction by storing the
final amount of fuel dispensed. 8. Reporting the fueling
transaction to a processing location for inventory analysis or
other analysis.
There are two types of authorization procedures for determining
whether an identified user is permitted to obtain fuel from a
fueling facility. Fuel access control unit 510 may use either or
both of these authorization procedures. For the first method of
authorization, external authorization, fuel access control unit 510
collects the user's information and forwards the collected
information to an outside agent to make a final decision as to
whether or not the identified user is permitted to obtain fuel from
fueling facility 500. The outside agent may return an approval,
along with fueling parameters (i.e., a maximum amount), or a
denial. Fuel access control unit 510 will then inform the user
whether or not fuel may be obtained. The outside agent may be
connected to fuel access control unit 510 via a dial-up telephone
line, a LAN or a direct communication link.
For the second method of authorization, internal authorization,
fuel access control unit 510 collects the user's information and
compares the collected information to a data table stored locally
to make the final decision as to whether or not to allow fueling.
The locally stored table may return an approval, along with fueling
parameters (i.e., a maximum amount), or a denial. Fuel access
control unit 510 will then inform the user whether or not fuel may
be obtained. The locally stored table may be housed directly in
fuel access control unit 510, in a control device at the fueling
facility such as on-site processor 530 or carried on the access
medium (e.g., card 520) used to request fueling authorization. The
locally stored table may also be imbedded directly in fuel access
control unit 510 or accessed via a LAN inside the fueling
facility's building 540.
Fuel access control unit 510 functions as an additional point of
sale (POS) device, similar to sales recording device 71 (FIG. 1).
Fuel access control unit 510 responds to requests for hose status
and totalizer and meter values in the same manner as a POS device.
Fuel access control unit 510 also monitors each hose 566 and tracks
status changes in the hose, including indications that the hose is
idle, that a request for access is in process, that the use of hose
566 has been authorized, that the hose has been taken off its hook,
that dispensing pump 560 is dispensing fuel with hose 566 removed
from its hook, and that the dispensing pump has been turned off and
the hose is idle again.
Each detailed transaction that is completed by fuel access control
unit 510 may be retrieved by on-site processor 530 from memory 567
upon completion of the transaction. The transaction information may
be stored in processor 530 for further analysis. Further, based on
the stored, detailed transaction information, a detailed site
dispensing audit can be performed. Such a site dispensing audit
would determine whether the volume claimed to be dispensed by fuel
access control unit 510 actually represent the volume change in the
UST or AST during the same period as calculated by on-site
processor 530.
In conventional cardlock applications as well as other transaction
authorization procedures, the processing methods assume that the
volume as determined by fuel access control unit 510 is accurate,
but have no way of determining if any errors in calculating the
volume have occurred. A fuel access control system interfaced
directly with an on-site processor 530 that receives data from an
automatic tank gauge 580 may also experience similar errors
associated with conventional inventory control practices. By
contrast, an enhanced, integrated fuel access control unit 510 may
include an accurate analysis of the state of hose 566. Such an
integrated fuel access control unit 510 may avoid the occurrence of
dispensing pump 560 being properly accessed and enabled by
authorization control system 562, but appearing not to be
dispensing fuel. From the point of view of fuel access control unit
510, the user may have simply changed his mind about purchasing
fuel. However, from the perspective of on-site processor 530, a
determination can be made about the dispensing pump's activity by
analyzing the tank activity and comparing that information to the
activity of totalizer 568 and meter 569. Further, although other
hoses may be actively dispensing fuel during the same period,
on-site processor 530 may track all hose activity independently for
analysis.
Other embodiments are within the scope of the claims.
* * * * *